English | 繁體

Search in the results

UM

Browse/Search Results:  1-10 of 14 Help

Selected(0)Clear Items/Page:    Sort:
A 90.7-nW Vibration-Based Condition Monitoring Chip Featuring a Digital Compute-in-Memory-Based DNN Accelerator Using an Ultra-Low-Power 13T-SRAM Cell Journal article
Zhang, Haochen, Yu, Wei Han, Yang, Zhizhan, Un, Ka Fai, Yin, Jun, Martins, Rui P., Mak, Pui In. A 90.7-nW Vibration-Based Condition Monitoring Chip Featuring a Digital Compute-in-Memory-Based DNN Accelerator Using an Ultra-Low-Power 13T-SRAM Cell[J]. IEEE JOURNAL OF SOLID-STATE CIRCUITS, 2024.
Authors:  Zhang, Haochen;  Yu, Wei Han;  Yang, Zhizhan;  Un, Ka Fai;  Yin, Jun; et al.
Favorite | TC[WOS]:0 TC[Scopus]:0  IF:4.6/5.6 | Submit date：2024/08/05
13t-sram  Accelerometer Sensor  Compute-in-memory (Cim)  Deep Neural Network (Dnn)  Feature Extractor  Internet-of-things  Ultra-low Power (Ulp)  Vibration-based Condition Monitoring (Vbcm)  
Long-term hourly air quality data bridging of neighboring sites using automated machine learning: A case study in the Greater Bay area of China Journal article
Wu, Boxi, Wu, Cheng, Ye, Yuchen, Pei, Chenglei, Deng, Tao, Li, Yong Jie, Lu, Xingcheng, Wang, Lei, Hu, Bin, Li, Mei, Wu, Dui. Long-term hourly air quality data bridging of neighboring sites using automated machine learning: A case study in the Greater Bay area of China[J]. Atmospheric Environment, 2024, 321, 120347.
Authors:  Wu, Boxi;  Wu, Cheng;  Ye, Yuchen;  Pei, Chenglei;  Deng, Tao; et al.
Favorite | TC[WOS]:1 TC[Scopus]:3  IF:4.2/4.4 | Submit date：2024/04/02
Air Pollution  Data Gap  Long-term Trend  Machine Learning  Monitoring Network  
Analyzing students’ performance in computerized formative assessments to optimize teachers’ test administration decisions using deep learning frameworks Journal article
Shin, Jinnie, Chen, Fu, Lu, Chang, Bulut, Okan. Analyzing students’ performance in computerized formative assessments to optimize teachers’ test administration decisions using deep learning frameworks[J]. Journal of Computers in Education, 2021, 9(1), 71-91.
Authors:  Shin, Jinnie;  Chen, Fu;  Lu, Chang;  Bulut, Okan
Favorite | TC[WOS]:15 TC[Scopus]:23  IF:4.3/4.8 | Submit date：2022/03/28
Computerized Formative Assessment  Long Short-term Memory Network  Progress Monitoring  Time Series Clustering  
Acoustic emission wave classification for rail crack monitoring based on synchrosqueezed wavelet transform and multi-branch convolutional neural network Journal article
Dan Li, Yang Wang, Wang Ji Yan, Wei Xin Ren. Acoustic emission wave classification for rail crack monitoring based on synchrosqueezed wavelet transform and multi-branch convolutional neural network[J]. Structural Health Monitoring, 2021, 20(4), 1563-1582.
Authors:  Dan Li;  Yang Wang;  Wang Ji Yan;  Wei Xin Ren
Favorite | TC[WOS]:71 TC[Scopus]:75  IF:5.7/6.8 | Submit date：2021/03/11
Rail  Crack Monitoring  Acoustic Emission  Classification  Synchrosqueezed Wavelet Transform  Multi-branch Convolutional Neural Network  
Deep learning-based crack identification for steel pipelines by extracting features from 3d shadow modeling Journal article
Altabey, Wael A., Noori, Mohammad, Wang, Tianyu, Ghiasi, Ramin, Wu, Zhishen. Deep learning-based crack identification for steel pipelines by extracting features from 3d shadow modeling[J]. Applied Sciences (Switzerland), 2021, 11(13), 6063.
Authors:  Altabey, Wael A.;  Noori, Mohammad;  Wang, Tianyu;  Ghiasi, Ramin;  Wu, Zhishen
Favorite | TC[WOS]:20 TC[Scopus]:29  IF:2.5/2.7 | Submit date：2021/12/08
3d Shadow Modeling  Automatic Crack Identification  Convolutional Neural Network (Cnn)  Deep Learning  Structural Health Monitoring (Shm)  
Acoustic emission wave classification for rail crack monitoring based on synchrosqueezed wavelet transform and multi-branch convolutional neural network Journal article
Li, D., Wang, Y., Yan, W., Ren, W.X.. Acoustic emission wave classification for rail crack monitoring based on synchrosqueezed wavelet transform and multi-branch convolutional neural network[J]. Structural Health Monitoring, 2021, 20(4), 1563-1582.
Authors:  Li, D.;  Wang, Y.;  Yan, W.;  Ren, W.X.
Favorite | TC[WOS]:71 TC[Scopus]:75  IF:5.7/6.8 | Submit date：2022/08/21
Rail  Crack Monitoring  Acoustic Emission  Classification  Synchrosqueezed Wavelet Transform  Multi-branch Convolutional Neural Network  
Deep Learning-Based Crack Identification for Steel Pipelines by Extracting Features from 3D Shadow Modeling Journal article
Altabey, W. A., Noori, M., Wang, T., Ghiasi, R.. Deep Learning-Based Crack Identification for Steel Pipelines by Extracting Features from 3D Shadow Modeling[J]. Applied Sciences, 2021, 1-21.
Authors:  Altabey, W. A.;  Noori, M.;  Wang, T.;  Ghiasi, R.
Favorite |   IF:2.5/2.7 | Submit date：2022/08/30
Deep Learning  Automatic Crack Identification  Convolutional Neural Network (Cnn)  3d Shadow Modeling  Structural Health Monitoring (Shm)  
Hierarchical outlier detection approach for online distributed structural identification Journal article
Ke Huang, Ka-Veng Yuen. Hierarchical outlier detection approach for online distributed structural identification[J]. Structural Control and Health Monitoring, 2020, 27(11), e2623.
Authors:  Ke Huang;  Ka-Veng Yuen
Favorite | TC[WOS]:8 TC[Scopus]:9  IF:4.6/5.5 | Submit date：2021/03/09
Bayesian  Hierarchical Detection  Online Distributed Identification  Outlier Detection  Structural Health Monitoring  Wireless Sensor Network  
Node Deployment of Marine Monitoring Networks: A Multiobjective Optimization Scheme Journal article
Duan,Jian Li, Lin,Bin, Cai,Lin X., Liu,Yu Xiang, Wu,Yuan. Node Deployment of Marine Monitoring Networks: A Multiobjective Optimization Scheme[J]. Sensors (Switzerland), 2020, 20(16), 4480.
Authors:  Duan,Jian Li;  Lin,Bin;  Cai,Lin X.;  Liu,Yu Xiang;  Wu,Yuan
Favorite | TC[WOS]:2 TC[Scopus]:6  IF:3.4/3.7 | Submit date：2021/03/11
Ant Colony Algorithm  Gurobi  Marine Monitoring Networks  Multiobjective Optimization  Network Deployment  
Predicting concentration levels of air pollutants by transfer learning and recurrent neural network Journal article
Iat Hang Fong, Tengyue Li, Simon Fong, Raymond K. Wong, Antonio J. Tallón-Ballesteros. Predicting concentration levels of air pollutants by transfer learning and recurrent neural network[J]. Knowledge-Based Systems, 2020, 192, 105622.
Authors:  Iat Hang Fong;  Tengyue Li;  Simon Fong;  Raymond K. Wong;  Antonio J. Tallón-Ballesteros
Favorite | TC[WOS]:56 TC[Scopus]:61  IF:7.2/7.4 | Submit date：2021/03/09
Forecasting  Environment Monitoring  Transfer Learning  Recurrent Neural Network  Airborne Particle Matter